The invention relates to a controller for a motor vehicle for actively influencing the handling of the motor vehicle. The invention also relates to a motor vehicle with such a controller. Lastly, the invention also provides a method for configuring the controller.
With the controller according to the invention, an estimated value of an operating variable that is not directly measurable in the motor vehicle may be provided. The term “observer device” refers here to a system that provides estimated values for the required additional operating variables that can not be directly measured or observed from known input variables that are formed by sensor signals. An observer of the observer device hereby reproduces the handling of the motor vehicle in a model according to which the state variables change commensurate with the sensor signals. The values of the state variables can then be used as estimated values for operating parameters that cannot be measured. A controller with such observer device is known for example from DE 195 15 046 A1
In general, controllers are employed in a motor vehicle, for example, for superimposed steering, rear wheel steering, torque vectoring (splitting the driving torque among individual wheels of the motor vehicle), driving or braking individual wheels, active stabilizers or damper control. The individual actuators for intervening in the handling are usually independently controlled, i.e. the controllers for the individual actuators decide independently which control action is necessary for the current driving situation. It is hereby assumed that there is a so-called peaceful coexistence, i.e. that there is no intervention overlap caused by simultaneous activation of different actuators, which could lead to an unstable handling. However, this makes it necessary to limit the degree of intervention, i.e. the magnitude of the intervention, in the individual actuators to such an extent that the driving stability is not compromised in any situation. As a result, the potential of the existing chassis control systems falls far behind the possibilities that would be available in individual, specific driving situations if the corresponding actuator were allowed to intervene more strongly. In order to be able to fully exploit the potential of a chassis control system for individual driving situations, a central control facility is therefore provided in the motor vehicle according to the so-called Global Chassis Control approach (GCC), which takes over the control of all existing actuators. A controller with a Global Chassis Control is known for example from DE 10 2007 020 169 A1.
A problem with conventional Global Chassis Control approaches is that a model for the handling of the motor vehicle must be relied upon in order to determine a combination of actuator interventions that is best suited for influencing the handling. A single-track model or two-track model is typically used. However, such a model is usually based on an assumption or on linear relationships, which are only present in relatively moderate driving situations. Such model loses its accuracy describing the real conditions with increasing proximity to a boundary region, such as tight cornering at high speed, thus making it impossible to make a reliable assessment of the vehicle stability or the effect of a control intervention on the handling. The future handling as a result of an intervention can then also not be predicted.
The difficulties that arise when planning an intervention when multiple actuators are available will now be discussed again in more detail with reference to the following examples. In an understeering situation, for example, the active front wheel steering is not suitable for vehicle stabilization. Conversely, in oversteering situations, rear wheel steering is not the best choice. However, the front wheel steering angle has a small effect even in an understeering situation and the rear-wheel steering has also a small effect on the handling in an oversteering situation. However, there is typically no way to estimate the magnitude of the effect while driving. Consequently, when a plurality of actuators for influencing the handling are present, an optimal distribution of the adjusted desired variables, for example, a yaw rate, a slip angle or a lateral acceleration on the respective actuators is also not possible.
DE 10 2008 030 667 A1 discloses a method and an apparatus for estimating parameters of a vehicle movement dynamics control system. Slip angle resistance is, among others, estimated as a parameter. The estimated values are required for solving of a system of differential equations using a numerical integration method. Another unknown variable of the equation system is the tire slip angle of the vehicle.
WO 99/67115 A1 describes a control circuit for controlling the driving stability of a vehicle, wherein a tire slip resistance of one or more tires is monitored to determine whether these parameters deviate from standard parameters. In this control circuit, the tire slip angle velocity is detected as a measured input variable.
DE 43 25 413 A1 describes a method for determining the tire slip angle of a vehicle. The tire slip angle is determined from the ratio of the vehicle's lateral velocity and the vehicle's longitudinal velocity. The vehicle's longitudinal velocity is measured, and the vehicle lateral velocity is calculated from the vehicle's lateral acceleration by an observer device.
EP 1 743 819 A1 describes a method and an arrangement for determining the yaw and roll motion of a vehicle. By using interpolation functions, the yaw rate, the average vehicle speed and the lateral acceleration measured at the front of the vehicle are used as parameters. Correction coefficients used here are determined by a manufacturer in comprehensive test series, wherein the vehicle is also subjected to extreme driving conditions and the resulting values for the yaw rate or yaw acceleration and the resulting values for the lateral acceleration are each compared with measured values from a yaw rate sensor. The optimal coefficients for the interpolation functions are determined by a curve fit to the corresponding data points and thereafter permanently stored.